Periodic Steady-State Control of a Handkerchief-Spinning Task Using a Parallel Anti-Parallelogram Tendon-driven Wrist

2026-04-20

AI Summary

This study addresses the challenge of high-dynamic, periodic manipulation of deformable objects—such as handkerchiefs—governed by nonlinear dynamics, frictional contact, and boundary constraints. To this end, the authors propose a task-specific, low-inertia, decoupled dexterous wrist mechanism featuring a parallel anti-parallelogram tendon-driven architecture. The approach integrates a mass-spring-based deformable body model within a hierarchical control framework comprising high- and low-level controllers. Experimental results demonstrate robust transition from rest to steady-state rotational manipulation, achieving approximately 99% handkerchief unfolding efficiency and a fingertip trajectory tracking error of 2.88 mm in root-mean-square (RMSE). These outcomes validate the effectiveness and novelty of the proposed mechanical design and control strategy for dynamic manipulation of complex deformable objects.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Low Level & Physics-based Vision
• Humans and AI: Interaction Techniques and Devices

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Spinning flexible objects, exemplified by traditional Chinese handkerchief performances, demands periodic steady-state motions under nonlinear dynamics with frictional contacts and boundary constraints. To address these challenges, we first design an intuitive dexterous wrist based on a parallel anti-parallelogram tendon-driven structure, which achieves 90 degrees omnidirectional rotation with low inertia and decoupled roll-pitch sensing, and implement a high-low level hierarchical control scheme. We then develop a particle-spring model of the handkerchief for control-oriented abstraction and strategy evaluation. Hardware experiments validate this framework, achieving an unfolding ratio of approximately 99% and fingertip tracking error of RMSE = 2.88 mm in high-dynamic spinning. These results demonstrate that integrating control-oriented modeling with a task-tailored dexterous wrist enables robust rest-to-steady-state transitions and precise periodic manipulation of highly flexible objects. More visualizations: https://slowly1113.github.io/icra2026-handkerchief/

Problem

• periodic steady-state control
• flexible object manipulation
• nonlinear dynamics
• frictional contacts
• boundary constraints

Innovation

• tendon-driven wrist
• periodic steady-state control
• flexible object manipulation
• particle-spring model
• hierarchical control